检索结果-内蒙古大学图书馆

SCORES: Shape Composition with Recursive Substructure Priors

ACM TRANSACTIONS ON GRAPHICS 2018年第6期37卷 211-211页

作者： Zhu, Chenyang Xu, Kai Chaudhuri, Siddhartha Yi, Renjiao Zhang, Hao Simon Fraser Univ Burnaby BC Canada Natl Univ Def Technol Changsha Hunan Peoples R China Princeton Univ Princeton NJ 08544 USA Adobe Res San Jose CA USA Indian Inst Technol Bombay Maharashtra India

We introduce SCORES, a recursive neural network for shape composition. Our network takes as input sets of parts from two or more source 3D shapes and a rough initial placement of the parts. It outputs an optimized part structure for the composed shape, leading to high-quality geometry construction. A unique feature of our composition network is that it is not merely learning how to connect parts. Our goal is to produce a coherent and plausible 3D shape, despite large incompatibilities among the input parts. The network may significantly alter the geometry and structure of the input parts and synthesize a novel shape structure based on the inputs, while adding or removing parts to minimize a structure plausibility loss. We design SCORES as a recursive autoencoder network. During encoding, the input parts are recursively grouped to generate a root code. During synthesis, the root code is decoded, recursively, to produce a new, coherent part assembly. Assembled shape structures may be novel, with little global resemblance to training exemplars, yet have plausible substructures. SCORES therefore learns a hierarchical substructure shape prior based on per-node losses. It is trained on structured shapes from ShapeNet, and is applied iteratively to reduce the plausibility loss. We show results of shape composition from multiple sources over different categories of man-made shapes and compare with state-of-the-art alternatives, demonstrating that our network can significantly expand the range of composable shapes for assembly-based modeling.

关键词： shape composition recursive neural network autoencoder structural synthesis substructure prior

来源：评论

学校读者我要写书评

暂无评论

Self-Taught Feature Learning for Hyperspectral Image Classification

引用

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2017年第5期55卷 2693-2705页

作者： Kemker, Ronald Kanan, Christopher Rochester Inst Technol Carlson Ctr Imaging Sci Rochester NY 14623 USA

In this paper, we study self-taught learning for hyperspectral image (HSI) classification. Supervised deep learning methods are currently state of the art for many machine learning problems, but these methods require large quantities of labeled data to be effective. Unfortunately, existing labeled HSI benchmarks are too small to directly train a deep supervised network. Alternatively, we used self-taught learning, which is an unsupervised method to learn feature extracting frameworks from unlabeled hyperspectral imagery. These models learn how to extract generalizable features by training on sufficiently large quantities of unlabeled data that are distinct from the target data set. Once trained, these models can extract features from smaller labeled target data sets. We studied two self-taught learning frameworks for HSI classification. The first is a shallow approach that uses independent component analysis and the second is a three-layer stacked convolutional autoencoder. Our models are applied to the Indian Pines, Salinas Valley, and Pavia University data sets, which were captured by two separate sensors at different altitudes. Despite large variation in scene type, our algorithms achieve state-of-the-art results across all the three data sets.

关键词： autoencoder deep learning feature learning hyperspectral imaging independent component analysis (ICA) self-taught learning

来源：评论

学校读者我要写书评

暂无评论

Building energy retrofit index for policy making and decision support at regional and national scales

引用

APPLIED ENERGY 2017年 206卷 1062-1075页

作者： Khayatian, Fazel Sarto, Luca Dall'O, Giuliano Politecn Milan Dept Architecture Built Environm & Construct Engn Milan Italy

The vast data collected since the enforcement of building energy labelling in Italy has provided valuable information that is useful for planning the future of building energy efficiency. However, the indicators provided through energy certificates are not suitable to support decisions, which target building energy retrofit in a regional scale. Considering the bias of the energy performance index toward a building's shape, decisions based on this index will favor buildings with a specific geometric characteristics. This study tends to overcome this issue by introducing a new indicator, tailored to rank buildings based on retrofitable characteristics. The proposed framework is validated by a case study, in which a large dataset of office buildings are assigned with the new index. Results indicate that the proposed indicator succeeds to extract a single index, which is representative of all building characteristics subject to energy retrofit. A new labeling procedure is also compared with the conventional classification of buildings. It is observed that the proposed labels properly partitions the dataset, according to buildings' potential to undergo energy retrofit.

关键词： Energy performance certificate Building energy retrofit autoencoder Neural network Clustering

来源：评论

学校读者我要写书评

暂无评论

An ensemble learning framework for anomaly detection in building energy consumption

引用

ENERGY AND BUILDINGS 2017年 144卷 191-206页

作者： Araya, Daniel B. Grolinger, Katarina ElYamany, Hany F. Capretz, Miriam A. M. Bitsuamlak, Girma Western Univ Dept Elect & Comp Engn London ON N6A 5B9 Canada Western Univ Dept Civil & Environm Engn London ON N6A 5B9 Canada Suez Canal Univ Dept Comp Sci Ismailia Egypt

During building operation, a significant amount of energy is wasted due to equipment and human related faults. To reduce waste, today's smart buildings monitor energy usage with the aim of identifying abnormal consumption behaviour and notifying the building manager to implement appropriate energy saving procedures. To this end, this research proposes a new pattern-based anomaly classifier, the collective contextual anomaly detection using sliding window (CCAD-SW) framework. The CCAD-SW framework identifies anomalous consumption patterns using overlapping sliding windows. To enhance the anomaly detection capacity of the CCAD-SW, this research also proposes the ensemble anomaly detection (EAD) framework. The EAD is a generic framework that combines several anomaly detection classifiers using majority voting. To ensure diversity of anomaly classifiers, the EAD is implemented by combining pattern-based (e.g., CCAD-SW) and prediction-based anomaly classifiers. The research was evaluated using real-world data provided by Powersmiths, located in Brampton, Ontario, Canada. Results show that the EAD framework improved the sensitivity of the CCAD-SW by 3.6% and reduced false alarm rate by 2.7%. (C) 2017 The Author(s). Published by Elsevier B.V.

关键词： Anomaly detection Ensemble learning autoencoder Support vector regression Random forest Building energy consumption

来源：评论

学校读者我要写书评

暂无评论

Deeply Learned View-Invariant Features for Cross-View Action Recognition

引用

IEEE TRANSACTIONS ON IMAGE PROCESSING 2017年第6期26卷 3028-3037页

作者： Kong, Yu Ding, Zhengming Li, Jun Fu, Yun Northeastern Univ Dept ECE Boston MA 02115 USA Northeastern Univ Dept ECE Coll Comp & Informat Sci Boston MA 02115 USA

Classifying human actions from varied views is challenging due to huge data variations in different views. The key to this problem is to learn discriminative view-invariant features robust to view variations. In this paper, we address this problem by learning view-specific and view-shared features using novel deep models. View-specific features capture unique dynamics of each view while view-shared features encode common patterns across views. A novel sample-affinity matrix is introduced in learning shared features, which accurately balances information transfer within the samples from multiple views and limits the transfer across samples. This allows us to learn more discriminative shared features robust to view variations. In addition, the incoherence between the two types of features is encouraged to reduce information redundancy and exploit discriminative information in them separately. The discriminative power of the learned features is further improved by encouraging features in the same categories to be geometrically closer. Robust view-invariant features are finally learned by stacking several layers of features. Experimental results on three multi-view data sets show that our approaches outperform the state-of-the-art approaches.

关键词： Action recognition autoencoder multi-view learning view-invariant features

来源：评论

学校读者我要写书评

暂无评论

Supervised deep learning embeddings for the prediction of cervical cancer diagnosis

引用

PEERJ COMPUTER SCIENCE 2018年第5期4卷 e154页

作者： Fernandes, Kelwin Chicco, Davide Cardoso, Jaime S. Fernandes, Jessica INESC TEC Inst Engn Sistemas & Comp Tecnol & Ciencia Porto Portugal Univ Porto Porto Portugal Princess Margaret Canc Ctr Toronto ON Canada Univ Cent Venezuela Caracas Venezuela

Cervical cancer remains a significant cause of mortality all around the world, even if it can be prevented and cured by removing affected tissues in early stages. Providing universal and efficient access to cervical screening programs is a challenge that requires identifying vulnerable individuals in the population, among other steps. In this work, we present a computationally automated strategy for predicting the outcome of the patient biopsy, given risk patterns from individual medical records. We propose a machine learning technique that allows a joint and fully supervised optimization of dimensionality reduction and classification models. We also build a model able to highlight relevant properties in the low dimensional space, to ease the classification of patients. We instantiated the proposed approach with deep learning architectures, and achieved accurate prediction results (top area under the curve AUC = 0.6875) which outperform previously developed methods, such as denoising autoencoders. Additionally, we explored some clinical findings from the embedding spaces, and we validated them through the medical literature, making them reliable for physicians and biomedical researchers.

关键词： Dimensionality reduction Health-care informatics Denoising autoencoder autoencoder Biomedical informatics Binary classification Deep learning Cervical cancer Artificial neural networks Health informatics

来源：评论

学校读者我要写书评

暂无评论

A novel passenger flow prediction model using deep learning methods

引用

TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2017年 84卷 74-91页

作者： Liu, Lijuan Chen, Rung-Ching Xiamen Univ Technol Coll Comp & Informat Engn 600 Ligong Rd Xiamen Fujian Peoples R China Chaoyang Univ Technol Dept Informat Management 168 Jifeng E Rd Taichung Taiwan

Currently, deep learning has been successfully applied in many fields and achieved amazing results. Meanwhile, big data has revolutionized the transportation industry over the past several years. These two hot topics have inspired us to reconsider the traditional issue of passenger flow prediction. As a special structure of deep neural network (DNN), an autoencoder can deeply and abstractly extract the nonlinear features embedded in the input without any labels. By exploiting its remarkable capabilities, a novel hourly passenger flow prediction model using deep learning methods is proposed in this paper. Temporal features including the day of a week, the hour of a day, and holidays, the scenario features including inbound and outbound, and tickets and cards, and the passenger flow features including the previous average passenger flow and real-time passenger flow, are defined as the input features. These features are combined and trained as different stacked autoencoders (SAE) in the first stage. Then, the pre-trained SAE are further used to initialize the supervised DNN with the real-time passenger flow as the label data in the second stage. The hybrid model (SAE-DNN) is applied and evaluated with a case study of passenger flow prediction for four bus rapid transit (BRT) stations of Xiamen in the third stage. The experimental results show that the proposed method has the capability to provide a more accurate and universal passenger flow prediction model for different BRT stations with different passenger flow profiles. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Passenger flow Prediction Deep learning autoencoder BRT

来源：评论

学校读者我要写书评

暂无评论

Feed forward neural network with random quaternionic neurons

引用

SIGNAL PROCESSING 2017年 136卷 59-68页

作者： Minemoto, Toshifumi Isokawa, Teijiro Nishimura, Haruhiko Matsui, Nobuyuki Univ Hyogo Grad Sch Engn 2167 Shosha Himeji Hyogo 6712280 Japan Univ Hyogo Grad Sch Appl Informat Chuo Ku Computat Sci Ctr Bldg 5-7F 7-1-28 Minatojima Kobe Hyogo 6500047 Japan

A quaternionic extension of feed forward neural network, for processing multi-dimensional signals, is proposed in this paper. This neural network is based on the three layered network with random weights, called Extreme Learning Machines (ELMs), in which iterative least-mean-square algorithms are not required for training networks. All parameters and variables in the proposed network are encoded by quaternions and operations among them follow the quaternion algebra. Neurons in the proposed network are expected to operate multidimensional signals as single entities, rather than real-valued neurons deal with each element of signals independently. The performances for the proposed network are evaluated through two types of experiments: classifications and reconstructions for color images in the CIFAR-10 dataset. The experimental results show that the proposed networks are superior in terms of classification accuracies for input images than the conventional (real-valued) networks with similar degrees of freedom. The detailed investigations for operations in the proposed networks are conducted.

关键词： Quaternion Feed forward neural network Extreme learning machine Classification autoencoder Affine transformation

来源：评论

学校读者我要写书评

暂无评论

Deep learning based matrix completion

引用

NEUROCOMPUTING 2017年 266卷 540-549页

作者： Fan, Jicong Chow, Tommy City Univ Hong Kong Dept Elect Engn Hong Kong Hong Kong Peoples R China

Previous matrix completion methods are generally based on linear and shallow models where the given incomplete matrices are of low-rank and the data are assumed to be generated by linear latent variable models. In this paper, we first propose a novel method called autoencoder based matrix completion (AEMC). The main idea of AEMC is to utilize the partially observed data to learn and construct a nonlinear latent variable model in the form of autoencoder. The hidden layer of the autoencoder has much fewer units than the visible layers do. Meanwhile, the unknown entries of the data are recovered to fit the nonlinear latent variable model. Based on AEMC, we further propose a deep learning based matrix completion (DLMC) method. In DLMC, AEMC is used as a pre-training step for both the missing entries and network parameters;the hidden layer of AEMC is then used to learn stacked autoencoders (SAES) with greedy layer-wise training;finally, fine-tuning is carried out on the deep network formed by AEMC and SAES to obtain the missing entries of the data and the parameters of the network. In addition, we also provide out-of-sample extensions for AEMC and DLMC to recover online incomplete data. AEMC and DLMC are compared with state-of-the-art methods in the tasks of synthetic matrix completion, image inpainting, and collaborative filtering. The experimental results verify the effectiveness and superiority of the proposed methods. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Matrix completion autoencoder deep learning out-of-sample extension image inpainting collaborative filtering

来源：评论

学校读者我要写书评

暂无评论

A new near-lossless EEG compression method using ANN-based reconstruction technique

引用

COMPUTERS IN BIOLOGY AND MEDICINE 2017年 87卷 87-94页

作者： Hejrati, Behzad Fathi, Abdolhossein Abdali-Mohammadi, Fardin Razi Univ Dept Comp Engn & Informat Technol Kermanshah Iran

Compression algorithm is an essential part of Telemedicine systems, to store and transmit large amount of medical signals. Most of existing compression methods utilize fixed transforms such as discrete cosine transform (DCT) and wavelet and usually cannot efficiently extract signal redundancy especially for non-stationary signals such as electroencephalogram (EEG). In this paper, we first propose learning-based adaptive transform using combination of DCT and artificial neural network (ANN) reconstruction technique. This adaptive ANN-based transform is applied to the DCT coefficients of EEG data to reduce its dimensionality and also to estimate the original DCT coefficients of EEG in the reconstruction phase. To develop a new near lossless compression method, the difference between the original DCT coefficients and estimated ones are also quantized. The quantized error is coded using Arithmetic coding and sent along with the estimated DCT coefficients as compressed data. The proposed method was applied to various datasets and the results show higher compression rate compared to the state-of-the-art methods.

关键词： Near-lossless compression autoencoder EEG signals Dimensionality reduction

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：